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import io
from deepface import DeepFace
import pandas as pd
import gradio as gr
import matplotlib.pyplot as plt
import requests, validators
import torch
import pathlib
from PIL import Image
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
def get_original_image(url_input):
"""Extract image from URL"""
if validators.url(url_input):
image = Image.open(requests.get(url_input, stream=True).raw)
return image
def face_verification(img1, img2, dist,model,detector):
"""Check the similarity of 2 images"""
try:
result = DeepFace.verify(img1_path=img1,img2_path=img2,distance_metric=dist,model_name=model,detector_backend=detector)
except:
result = DeepFace.verify(img1_path=img1,img2_path=img2,distance_metric=dist,model_name=model,detector_backend=detector,\
enforce_detection=False)
return result['verified'].astype(str),round(result['distance'],2),result['threshold'],result['model'],result['similarity_metric']
def facial_analysis(img1, detector):
"""Determine emotion, race, gender and age from models"""
try:
#facial analysis
obj = DeepFace.analyze(img_path = img1, actions = ['age', 'gender', 'race', 'emotion'],detector_backend=detector)
except:
obj = DeepFace.analyze(img_path = img1, actions = ['age', 'gender', 'race', 'emotion'],detector_backend=detector,\
enforce_detection=False)
print(obj)
return obj[0]['age'],obj[0]['dominant_gender'],obj[0]['dominant_race'],obj[0]['dominant_emotion']
def face_recognition(img1,dir_loc,model,dist,detector):
"""Facial recognition given a database or folder location with images"""
#face recognition
rec = DeepFace.find(img_path = img_1, db_path = dir_loc,distance_metric=dist,model_name=model,detector_backend=detector)
return rec
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def set_example_url(example: list) -> dict:
return gr.Textbox.update(value=example[0]), gr.Image.update(value=get_original_image(example[0]))
title = """<h1 id="title">DeepFace for Facial Recognition and Analysis</h1>"""
description = """
Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace, Dlib and SFace.
Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
Please click on the Github link for more information: [DeepFace](https://github.com/serengil/deepface)
This space captures facial verification which determines if 2 facial images are the same person and the facial attribute analysis which predicts age,gender, emotion and race. The attribute analysis for age and race is a hit and miss based on my personal experience and the reported test accuracy from the Github page is 68% for race prediction. The age prediction model got ± 4.65 MAE.
The prediction models work better with images that mainly show the face.
"""
models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]
metrics = ["cosine", "euclidean", "euclidean_l2"]
backends = ['opencv', 'ssd', 'mtcnn', 'retinaface', 'mediapipe']
urls = [["https://media.vanityfair.com/photos/6036a15657f37ea4415256d2/master/w_2560%2Cc_limit/1225292516",\
"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQSuPVx0JaEW2yp4mT8ZwqFANMT3RhoxcwuuGLhnKgxsA&s"]]
url = [['https://media.vanityfair.com/photos/6036a15657f37ea4415256d2/master/w_2560%2Cc_limit/1225292516']]
all_images = [[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.j*g'))]
nick = [[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*nick*.j*g'))]
nicks_images = [[i[0] for i in nick]]
twitter_link = """
[](https://twitter.com/nickmuchi)
"""
css = '''
h1#title {
text-align: center;
}
'''
demo = gr.Blocks(css=css)
with demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown(twitter_link)
model_options = gr.Dropdown(choices=models,label='Facial Recognition Models',value=models[1],show_label=True)
metric_options = gr.Radio(choices=metrics,label='Distance Metric', value=metrics[0],show_label=True)
backends_options = gr.Dropdown(choices=backends,label='Face Detector',value=backends[-2],show_label=True)
with gr.Tabs():
with gr.TabItem('Facial Recognition'):
with gr.Tabs():
with gr.TabItem("URL Images"):
with gr.Row():
with gr.Column():
url_input_1_fr = gr.Textbox(lines=2,label='Image URL 1')
url_image_1_fr = gr.Image(label='Image 1',shape=(550,550),interactive=False)
url_input_1_fr.change(get_original_image, url_input_1_fr, url_image_1_fr)
url_input_2 = gr.Textbox(lines=2,label='Image URL 2')
url_image_2 = gr.Image(label='Image 2',shape=(550,550),interactive=False)
url_input_2.change(get_original_image, url_input_2, url_image_2)
with gr.Column():
sim_from_url = gr.Label(label='Same Person')
dist_from_url = gr.Label(label = 'Distance')
thresh_from_url = gr.Label(label = 'Threshold to Verify')
model_from_url = gr.Label(label = 'Model Name')
metric_from_url = gr.Label(label = 'Similarity Metric')
with gr.Row():
example_url = gr.Examples(examples=urls,inputs=[url_input_1_fr,url_input_2])
url_but_fr = gr.Button('Verify')
with gr.TabItem("Upload Images"):
with gr.Row():
with gr.Column():
upload_image_1_fr = gr.Image(label='Image 1',shape=(550,550),interactive=True)
upload_image_2 = gr.Image(label='Image 2',shape=(550,550),interactive=True)
with gr.Column():
sim_from_upload = gr.Label(label='Same Person')
dist_from_upload = gr.Label(label = 'Distance')
thresh_from_upload = gr.Label(label = 'Threshold to Verify')
model_from_upload = gr.Label(label = 'Model Name')
metric_from_upload = gr.Label(label = 'Similarity Metric')
with gr.Row():
example_images = gr.Examples(examples =nicks_images,inputs=[upload_image_1_fr,upload_image_2])
up_but_fr = gr.Button('Verify')
with gr.TabItem('Facial Analysis'):
with gr.Tabs():
with gr.TabItem("URL Image"):
with gr.Row():
with gr.Column():
url_input_1_fa = gr.Textbox(lines=2,label='Enter valid image URL here..')
url_image_1_fa = gr.Image(label='Image 1',shape=(550,550))
url_input_1_fa.change(get_original_image, url_input_1_fa, url_image_1_fa)
with gr.Column():
age_from_url = gr.Label(label='Age')
gender_from_url = gr.Label(label = 'Gender')
emo_from_url = gr.Label(label = 'Emotion')
race_from_url = gr.Label(label = 'Race')
with gr.Row():
example_url = gr.Examples(examples=url,inputs=[url_input_1_fa])
url_but_fa = gr.Button('Analyze')
with gr.TabItem("Upload Image"):
with gr.Row():
with gr.Column():
upload_image_1_fa = gr.Image(label='Image 1',shape=(550,550))
with gr.Column():
age_from_upload = gr.Label(label='Age')
gender_from_upload = gr.Label(label = 'Gender')
emo_from_upload = gr.Label(label = 'Emotion')
race_from_upload = gr.Label(label = 'Race')
with gr.Row():
example_images = gr.Examples(examples =all_images,inputs=[upload_image_1_fa])
up_but_fa = gr.Button('Analyze')
with gr.TabItem("WebCam Image"):
with gr.Row():
with gr.Column():
web_image = gr.Image(label='WebCam Image',source='webcam',shape=(550,550),streaming=True)
with gr.Column():
age_from_web = gr.Label(label='Age')
gender_from_web = gr.Label(label = 'Gender')
emo_from_web = gr.Label(label = 'Emotion')
race_from_web = gr.Label(label = 'Race')
web_but_fa = gr.Button('Analyze')
url_but_fr.click(face_verification,inputs=[url_image_1_fr,url_image_2,metric_options,model_options,backends_options],\
outputs=[sim_from_url,dist_from_url,thresh_from_url,model_from_url,metric_from_url],queue=True)
up_but_fr.click(face_verification,inputs=[upload_image_1_fr,upload_image_2,metric_options,model_options,backends_options],\
outputs=[sim_from_upload,dist_from_upload,thresh_from_upload,model_from_upload,metric_from_upload],queue=True)
url_but_fa.click(facial_analysis,inputs=[url_image_1_fa,backends_options],\
outputs=[age_from_url,gender_from_url,race_from_url,emo_from_url],queue=True)
up_but_fa.click(facial_analysis,inputs=[upload_image_1_fa,backends_options],\
outputs=[age_from_upload,gender_from_upload,race_from_upload,emo_from_upload])
web_but_fa.click(facial_analysis,inputs=[web_image,backends_options],\
outputs=[age_from_web,gender_from_web,race_from_web,emo_from_web])
gr.Markdown("")
demo.launch(debug=True,enable_queue=True) |